{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import altair as alt\n",
    "import shap\n",
    "\n",
    "from interaction_effects.marginal import MarginalExplainer\n",
    "from interaction_effects import utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 1000\n",
    "d = 3\n",
    "batch_size = 50\n",
    "learning_rate = 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "W = np.array([-2.0, -3.0, 1.0])\n",
    "b = 0.0\n",
    "X = np.random.randn(n, d)\n",
    "y = np.dot(X, W) + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential()\n",
    "model.add(tf.keras.Input(shape=(3,), batch_size=batch_size))\n",
    "model.add(tf.keras.layers.Dense(1, activation=None, use_bias=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate)\n",
    "model.compile(optimizer=optimizer,\n",
    "              loss=tf.keras.losses.MSE,\n",
    "              metrics=[tf.keras.metrics.MeanAbsoluteError(), tf.keras.metrics.MeanSquaredError()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1000 samples\n",
      "Epoch 1/10\n",
      "1000/1000 - 0s - loss: 3.7512 - mean_absolute_error: 1.0685 - mean_squared_error: 3.7512\n",
      "Epoch 2/10\n",
      "1000/1000 - 0s - loss: 5.2663e-04 - mean_absolute_error: 0.0123 - mean_squared_error: 5.2663e-04\n",
      "Epoch 3/10\n",
      "1000/1000 - 0s - loss: 7.6488e-08 - mean_absolute_error: 1.4620e-04 - mean_squared_error: 7.6488e-08\n",
      "Epoch 4/10\n",
      "1000/1000 - 0s - loss: 1.1420e-11 - mean_absolute_error: 1.7865e-06 - mean_squared_error: 1.1420e-11\n",
      "Epoch 5/10\n",
      "1000/1000 - 0s - loss: 1.2544e-13 - mean_absolute_error: 2.4549e-07 - mean_squared_error: 1.2544e-13\n",
      "Epoch 6/10\n",
      "1000/1000 - 0s - loss: 1.2499e-13 - mean_absolute_error: 2.4343e-07 - mean_squared_error: 1.2499e-13\n",
      "Epoch 7/10\n",
      "1000/1000 - 0s - loss: 1.2504e-13 - mean_absolute_error: 2.3904e-07 - mean_squared_error: 1.2504e-13\n",
      "Epoch 8/10\n",
      "1000/1000 - 0s - loss: 8.6274e-14 - mean_absolute_error: 1.9143e-07 - mean_squared_error: 8.6274e-14\n",
      "Epoch 9/10\n",
      "1000/1000 - 0s - loss: 8.6583e-14 - mean_absolute_error: 1.9206e-07 - mean_squared_error: 8.6583e-14\n",
      "Epoch 10/10\n",
      "1000/1000 - 0s - loss: 8.6506e-14 - mean_absolute_error: 1.9210e-07 - mean_squared_error: 8.6506e-14\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fa7998cd198>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X, y, epochs=10, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W1104 17:05:04.217525 140359598237504 base_layer.py:1814] Layer dense is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.\n",
      "\n",
      "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
      "\n",
      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
      "\n",
      "100%|██████████| 20/20 [00:00<00:00, 22.19it/s]\n"
     ]
    }
   ],
   "source": [
    "primal_explainer = MarginalExplainer(model, X[20:], nsamples=800, representation='mobius')\n",
    "primal_effects = primal_explainer.explain(X[:20], verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 20/20 [00:00<00:00, 21.77it/s]\n"
     ]
    }
   ],
   "source": [
    "dual_explainer = MarginalExplainer(model, X[20:], nsamples=800, representation='comobius')\n",
    "dual_effects = dual_explainer.explain(X[:20], verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 20/20 [00:01<00:00, 12.58it/s]\n"
     ]
    }
   ],
   "source": [
    "average_explainer = MarginalExplainer(model, X[20:], nsamples=800, representation='average')\n",
    "average_effects = average_explainer.explain(X[:20], verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "shap_values = X * W[np.newaxis, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "774003b513bb482496d6989c7ec6740c",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=20), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "model_func = lambda x: model(x).numpy()\n",
    "kernel_explainer = shap.SamplingExplainer(model_func, X)\n",
    "kernel_shap = kernel_explainer.shap_values(X[:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df = pd.DataFrame({\n",
    "    'True SHAP Values':        shap_values[:20].flatten(),\n",
    "    'Sampled Primal Effects':  primal_effects.flatten(),\n",
    "    'Sampled Dual Effects':    dual_effects.flatten(),\n",
    "    'Sampled Average Effects': average_effects.flatten(),\n",
    "    'Kernel SHAP Values':      kernel_shap.flatten()\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<VegaLite 3 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(data_df).mark_point(filled=True).encode(\n",
    "    alt.X('True SHAP Values:Q'),\n",
    "    alt.Y(alt.repeat(\"column\"), type='quantitative')\n",
    ").properties(width=300, height=300).repeat(column=['Sampled Primal Effects', 'Sampled Dual Effects'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v3+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v3.2.1.json",
       "config": {
        "mark": {
         "tooltip": null
        },
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "datasets": {
        "data-d8b67342b53cef7580cdb3b173d5b235": [
         {
          "Kernel SHAP Values": -1.4263211712038588,
          "Sampled Average Effects": -1.6173045186698436,
          "Sampled Dual Effects": -1.5641750702261925,
          "Sampled Primal Effects": -1.6253183746337891,
          "True SHAP Values": -1.4896582317424687
         },
         {
          "Kernel SHAP Values": -1.7048704354330906,
          "Sampled Average Effects": -1.6534313490986825,
          "Sampled Dual Effects": -1.5571852959692478,
          "Sampled Primal Effects": -1.569989938735962,
          "True SHAP Values": -1.5659777967577193
         },
         {
          "Kernel SHAP Values": 1.0998874005207724,
          "Sampled Average Effects": 1.1500288009643556,
          "Sampled Dual Effects": 1.1127768421173097,
          "Sampled Primal Effects": 1.1102637434005738,
          "True SHAP Values": 1.0931222387747033
         },
         {
          "Kernel SHAP Values": 1.3567463976445848,
          "Sampled Average Effects": 1.4684699058532715,
          "Sampled Dual Effects": 1.278823413848877,
          "Sampled Primal Effects": 1.3148322677612305,
          "True SHAP Values": 1.4442079820179372
         },
         {
          "Kernel SHAP Values": 2.9541430471094094,
          "Sampled Average Effects": 2.9790642297267915,
          "Sampled Dual Effects": 2.9647518038749694,
          "Sampled Primal Effects": 3.1870367622375486,
          "True SHAP Values": 3.010205327873406
         },
         {
          "Kernel SHAP Values": -0.5319742808086076,
          "Sampled Average Effects": -0.5956086897850037,
          "Sampled Dual Effects": -0.5538166332244873,
          "Sampled Primal Effects": -0.6014271306991578,
          "True SHAP Values": -0.6067072582583516
         },
         {
          "Kernel SHAP Values": 2.2913384324694936,
          "Sampled Average Effects": 2.2285960006713865,
          "Sampled Dual Effects": 2.2565358161926268,
          "Sampled Primal Effects": 2.1984537982940675,
          "True SHAP Values": 2.3626084910167457
         },
         {
          "Kernel SHAP Values": 1.4864339872401344,
          "Sampled Average Effects": 1.5984789967536925,
          "Sampled Dual Effects": 1.5038383674621583,
          "Sampled Primal Effects": 1.3322387981414794,
          "True SHAP Values": 1.4452402771205546
         },
         {
          "Kernel SHAP Values": 0.5253090554041523,
          "Sampled Average Effects": 0.6217668581008912,
          "Sampled Dual Effects": 0.5569986534118653,
          "Sampled Primal Effects": 0.5511064291000366,
          "True SHAP Values": 0.5640234688394292
         },
         {
          "Kernel SHAP Values": -0.11781636095641707,
          "Sampled Average Effects": -0.19213902592658996,
          "Sampled Dual Effects": -0.20675999641418458,
          "Sampled Primal Effects": -0.3014854627847672,
          "True SHAP Values": -0.11993538099686668
         },
         {
          "Kernel SHAP Values": -3.29480224776163,
          "Sampled Average Effects": -3.0048598877340553,
          "Sampled Dual Effects": -3.1304656130075457,
          "Sampled Primal Effects": -3.3327982330322268,
          "True SHAP Values": -3.129784424849872
         },
         {
          "Kernel SHAP Values": 0.4798575956972414,
          "Sampled Average Effects": 0.40498370885849,
          "Sampled Dual Effects": 0.4210134220123291,
          "Sampled Primal Effects": 0.4696322703361511,
          "True SHAP Values": 0.38574905792884145
         },
         {
          "Kernel SHAP Values": -0.9626330711605985,
          "Sampled Average Effects": -1.0416764783859254,
          "Sampled Dual Effects": -1.0974843502044678,
          "Sampled Primal Effects": -1.1090154075622558,
          "True SHAP Values": -0.9646131623913786
         },
         {
          "Kernel SHAP Values": 3.14439361802514,
          "Sampled Average Effects": 3.241914727985859,
          "Sampled Dual Effects": 3.194953753948212,
          "Sampled Primal Effects": 3.0643068027496336,
          "True SHAP Values": 3.1835327674324145
         },
         {
          "Kernel SHAP Values": -0.01368255909259735,
          "Sampled Average Effects": 0.05657502084970474,
          "Sampled Dual Effects": 0.04949051856994629,
          "Sampled Primal Effects": 0.043775062263011935,
          "True SHAP Values": 0.017949309389637926
         },
         {
          "Kernel SHAP Values": 2.65386453986084,
          "Sampled Average Effects": 2.7125129318237304,
          "Sampled Dual Effects": 2.6863937759399414,
          "Sampled Primal Effects": 2.6637241077423095,
          "True SHAP Values": 2.794771885102684
         },
         {
          "Kernel SHAP Values": 1.3634387229683949,
          "Sampled Average Effects": 1.4518313384056092,
          "Sampled Dual Effects": 1.3511653327941895,
          "Sampled Primal Effects": 1.5115167140960692,
          "True SHAP Values": 1.3991379899628065
         },
         {
          "Kernel SHAP Values": 1.2344161218302403,
          "Sampled Average Effects": 1.1355683779716492,
          "Sampled Dual Effects": 1.2082376670837403,
          "Sampled Primal Effects": 1.1638651657104493,
          "True SHAP Values": 1.1266001187544417
         },
         {
          "Kernel SHAP Values": 0.4637579765884741,
          "Sampled Average Effects": 0.37704583138227465,
          "Sampled Dual Effects": 0.5519155120849609,
          "Sampled Primal Effects": 0.4274804782867432,
          "True SHAP Values": 0.5778873710379528
         },
         {
          "Kernel SHAP Values": -2.6632784014813233,
          "Sampled Average Effects": -2.7004527258872986,
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",
      "text/plain": [
       "<VegaLite 3 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(data_df).mark_point(filled=True).encode(\n",
    "    alt.X('True SHAP Values:Q'),\n",
    "    alt.Y(alt.repeat(\"column\"), type='quantitative')\n",
    ").properties(width=300, height=300).repeat(column=['Sampled Average Effects', 'Kernel SHAP Values'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
